Narendra Kumar Shukla

Bio: Narendra Kumar Shukla is an academic researcher from Shiv Nadar University. The author has contributed to research in topic(s): Personal area network & Efficient energy use. The author has an hindex of 2, co-authored 2 publication(s) receiving 18 citation(s).

Utilization-based power consumption profiling in smartphones

Abstract: Energy cost of crowd-sourced continuous sensing is reported to be quite high. As the number of on-board active sensors increases, complications arise due to inter-sensor interactions. The energy-cost of the Smartphones is primarily due to wireless communications (in various modes, such as, cellular radio, GPS, Wi-Fi direct, and Bluetooth) and environmental sensing using its embedded sensors in a wireless personal area network setting. The existing popular on-device-online energy-cost profilers for Android Smartphones, namely, Amobisense and PowerTutor, are energy-hungry. In this paper, we report an efficient on-demand-online profiler, called pProf, that learns from offline-precomputed model parameters to reduce the online profiling cost. We have tested our proposed technique in a customized test-bed setup comprising of the Android Smart-phones with embedded sensors that also communicate with the neighborhood sensors on smart-wearables and Sensorcon's Sensordrone platform. Our experimental measurement studies demonstrate that, compared to the popular profilers, such as Amobisense and PowerTutor, pProf consumes typically 10–15% lesser energy.

Energy-Efficient Air Pollution Monitoring with Optimum Duty-Cycling on a Sensor Hub

Abstract: Air pollution monitoring systems with energy-intensive sensors cannot afford to sample frequently in order to maximize time between successive recharges. In this paper, we propose an energy-efficient machine learning based sensor duty-cycling method for a sensor hub receiving data from the air-pollution sensors. In particular, we demonstrate that temporal correlation of pollutant concentration can be exploited to select optimum sampling period of an energy-intensive sensor to reduce sensing energy consumption without losing much information. Support Vector Regression is used to predict the missing samples during the period sensor is turned off.

Power Consumption Analysis, Measurement, Management, and Issues: A State-of-the-Art Review of Smartphone Battery and Energy Usage

Abstract: The advancement and popularity of smartphones have made it an essential and all-purpose device. But lack of advancement in battery technology has held back its optimum potential. Therefore, considering its scarcity, optimal use and efficient management of energy are crucial in a smartphone. For that, a fair understanding of a smartphone’s energy consumption factors is necessary for both users and device manufacturers, along with other stakeholders in the smartphone ecosystem. It is important to assess how much of the device’s energy is consumed by which components and under what circumstances. This paper provides a generalized, but detailed analysis of the power consumption causes (internal and external) of a smartphone and also offers suggestive measures to minimize the consumption for each factor. The main contribution of this paper is four comprehensive literature reviews on: 1) smartphone’s power consumption assessment and estimation (including power consumption analysis and modelling); 2) power consumption management for smartphones (including energy-saving methods and techniques); 3) state-of-the-art of the research and commercial developments of smartphone batteries (including alternative power sources); and 4) mitigating the hazardous issues of smartphones’ batteries (with a details explanation of the issues). The research works are further subcategorized based on different research and solution approaches. A good number of recent empirical research works are considered for this comprehensive review, and each of them is succinctly analysed and discussed.

The rise of low-cost sensing for managing air pollution in cities

Abstract: Ever growing populations in cities are associated with a major increase in road vehicles and air pollution. The overall high levels of urban air pollution have been shown to be of a significant risk to city dwellers. However, the impacts of very high but temporally and spatially restricted pollution, and thus exposure, are still poorly understood. Conventional approaches to air quality monitoring are based on networks of static and sparse measurement stations. However, these are prohibitively expensive to capture tempo-spatial heterogeneity and identify pollution hotspots, which is required for the development of robust real-time strategies for exposure control. Current progress in developing low-cost micro-scale sensing technology is radically changing the conventional approach to allow real-time information in a capillary form. But the question remains whether there is value in the less accurate data they generate. This article illustrates the drivers behind current rises in the use of low-cost sensors for air pollution management in cities, whilst addressing the major challenges for their effective implementation.

Green Sensing and Communication: A Step Towards Sustainable IoT Systems

Abstract: With the advent of Internet of Things (IoT) devices, their reconfigurability, networking, task automation, and control ability have been a boost to the evolution of traditional industries such as health-care, agriculture, power, education, and transport. However, the quantum of data produced by the IoT devices poses serious challenges on its storage, communication, computation, security, scalability, and system’s energy sustainability. To address these challenges, the concept of green sensing and communication has gained importance. This article surveys the existing green sensing and communication approaches to realize sustainable IoT systems for various applications. Further, a few case studies are presented that aim to generate sensed traffic data intelligently as well as prune it efficiently without sacrificing the required service quality. Challenges associated with these green techniques, various open issues, and future research directions for improving the energy efficiency of the IoT systems are also discussed.

Modélisation Incrémentale des Processeurs Embarqués pour l'Estimation des Caractéristiques et le Diagnostic

Abstract: Les systemes-sur-puce (Systems on Chip, SoC) sont de plus en plus embarques dans des systemes a risque comme les systemes aeronautiques et les equipements de production d’energie. Cette evolution technologique permet un gain de temps et de performance, mais presente des limites en termes de fiabilite et de securite. Ainsi, le developpement d’outils de surveillance et de diagnostic des systemes electroniques embarques, en particuliers les SoC, est devenu l’un des verrous scientifiques a lever pour assurer une large utilisation de ces systemes dans les equipements a risque en toute securite. Ce travail de these s’inscrit dans ce contexte, et a pour objectif le developpement d’une approche de detection et identification des derives des performances des SoC embarques. L’approche proposee est basee sur un modele incremental, construit a partir de modules reutilisables et echangeables pour correspondre a la large gamme de SoC existants sur le marche. Le modele est ensuite utilise pour estimer un ensemble de caracteristiques relatives a l’etat de fonctionnement du SoC. L’algorithme de diagnostic developpe dans ce travail consiste a generer des indices de derives par la comparaison en ligne des caracteristiques estimees a celles mesurees. L’evaluation des residus et la prise de decision sont realisees par des methodes statistiques appropriees a la nature de chaque indice de derive. L’approche developpee a ete validee experimentalement sur des SoC differents, ainsi que sur un demonstrateur developpe dans le cadre de ce travail. Les resultats experimentaux obtenus, montrent l’efficacite et la robustesse de l’approche developpee

Data-driven optimizations in IoT: a new frontier of challenges and opportunities

Abstract: Internet of Things (IoT) has gained tremendous popularity with the recent fast-paced technological advances in embedded programmable electronic and electro-mechanical systems, miniaturization, and their networking ability. IoT is expected to change the way of human activities by extensively networked monitoring, automation, and control. However, widespread application of IoT is associated with numerous challenges on communication and storage requirements, energy sustainability, and security. Also, IoT data traffic as well as the service quality requirements are application-specific. Through a few practical example cases, this article presents IoT data driven unique communication approaches and optimization techniques to reduce the data handling footprint, leading to communication bandwidth, cloud storage, and energy saving, without compromising the service quality. Subsequently, it discusses newer challenges that are needed to be tackled, to make the IoT applications practically viable for their wide-ranging adoption.